Introducing Polymath: Convert any music library into a music production sample-library with ML
Polymath is a open-source tool i've developed that uses machine learning to convert any music library (e.g from Hard-Drive or YouTube) into a music production sample-library. The tool automatically separates songs into stems (beats, bass, etc.), quantizes them to the same tempo and beat-grid (e.g. 120bpm) and analyzes musical structure (e.g. verse, chorus, etc.), key (e.g C4, E3, etc.) and other infos (timbre, loudness, etc.). The result is a searchable sample library that streamlines the workflow for music producers, DJs, and ML devs.
Use-cases: Polymath makes it effortless to combine elements from different songs to create unique new compositions: Simply grab a beat from a Funkadelic track, a bassline from a Tito Puente piece, and fitting horns from a Fela Kuti song, and seamlessly integrate them into your DAW in record time. Using Polymath's search capability to discover related tracks, it is a breeze to create a polished, hour-long mash-up DJ set. For ML devs, Polymath simplifies the process of creating a large music dataset, for training generative models, etc.
More infos and download here: https://github.com/samim23/polymath